






'''simple docstring'''





import argparse
import collections
import json
import os
import re
import string
import sys

import numpy as np


_UpperCamelCase						:						List[str]  =					re.compile(R'\b(a|an|the)\b', re.UNICODE)

_UpperCamelCase						:						Optional[int]  =					None
def __UpperCAmelCase   (					)    ->   Optional[Any]:
			UpperCAmelCase_      :       List[Any]          =       argparse.ArgumentParser('''Official evaluation script for SQuAD version 2.0.'''		)
			parser.add_argument('''data_file'''			,       metavar='''data.json'''			,       help='''Input data JSON file.'''		)
			parser.add_argument('''pred_file'''			,       metavar='''pred.json'''			,       help='''Model predictions.'''		)
			parser.add_argument(
			    '''--out-file'''			,       '''-o'''			,       metavar='''eval.json'''			,       help='''Write accuracy metrics to file (default is stdout).'''		)
			parser.add_argument(
			    '''--na-prob-file'''			,       '''-n'''			,       metavar='''na_prob.json'''			,       help='''Model estimates of probability of no answer.'''		)
			parser.add_argument(
			    '''--na-prob-thresh'''			,       '''-t'''			,       type=A			,       default=1.0			,       help='''Predict "" if no-answer probability exceeds this (default = 1.0).'''			,       )
			parser.add_argument(
			    '''--out-image-dir'''			,       '''-p'''			,       metavar='''out_images'''			,       default=A			,       help='''Save precision-recall curves to directory.'''		)
			parser.add_argument('''--verbose'''			,       '''-v'''			,       action='''store_true'''		)
			if len(sys.argv		) == 1:
						parser.print_help()
						sys.exit(1		)
			return parser.parse_args()
def __UpperCAmelCase   (					A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Optional[Any]          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Union[str, Any]          =       bool(qa['''answers''']['''text''']		)
			return qid_to_has_ans
def __UpperCAmelCase   (					A					:			List[Any]		)    ->   Dict:

			def remove_articles(A					:			Dict		):
						return ARTICLES_REGEX.sub(''' '''			,       A		)

			def white_space_fix(A					:			str		):
						return " ".join(text.split()		)

			def remove_punc(A					:			Tuple		):
						UpperCAmelCase_      :       Dict          =       set(string.punctuation		)
						return "".join(ch for ch in text if ch not in exclude		)

			def lower(A					:			Any		):
						return text.lower()

			return white_space_fix(remove_articles(remove_punc(lower(A		)		)		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]		)    ->   Tuple:
			if not s:
						return []
			return normalize_answer(A		).split()
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Dict		)    ->   List[str]:
			return int(normalize_answer(A		) == normalize_answer(A		)		)
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			List[Any]		)    ->   str:
			UpperCAmelCase_      :       Tuple          =       get_tokens(A		)
			UpperCAmelCase_      :       Union[str, Any]          =       get_tokens(A		)
			UpperCAmelCase_      :       Dict          =       collections.Counter(A		) & collections.Counter(A		)
			UpperCAmelCase_      :       Optional[int]          =       sum(common.values()		)
			if len(A		) == 0 or len(A		) == 0:
						# If either is no-answer, then F1 is 1 if they agree, 0 otherwise
						return int(gold_toks == pred_toks		)
			if num_same == 0:
						return 0
			UpperCAmelCase_      :       int          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       1.0 * num_same / len(A		)
			UpperCAmelCase_      :       Any          =       (2 * precision * recall) / (precision + recall)
			return fa
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			str		)    ->   Union[str, Any]:
			UpperCAmelCase_      :       List[Any]          =       {}
			UpperCAmelCase_      :       str          =       {}
			for article in dataset:
						for p in article["paragraphs"]:
									for qa in p["qas"]:
												UpperCAmelCase_      :       Optional[Any]          =       qa['''id''']
												UpperCAmelCase_      :       List[str]          =       [t for t in qa['''answers''']['''text'''] if normalize_answer(A		)]
												if not gold_answers:
															# For unanswerable questions, only correct answer is empty string
															UpperCAmelCase_      :       Tuple          =       ['''''']
												if qid not in preds:
															print(F"Missing prediction for {qid}"		)
															continue
												UpperCAmelCase_      :       Tuple          =       preds[qid]
												# Take max over all gold answers
												UpperCAmelCase_      :       List[Any]          =       max(compute_exact(A			,       A		) for a in gold_answers		)
												UpperCAmelCase_      :       int          =       max(compute_fa(A			,       A		) for a in gold_answers		)
			return exact_scores, fa_scores
def __UpperCAmelCase   (					A					:			Optional[int]			,       A					:			Tuple			,       A					:			Optional[Any]			,       A					:			List[Any]		)    ->   Optional[int]:
			UpperCAmelCase_      :       Tuple          =       {}
			for qid, s in scores.items():
						UpperCAmelCase_      :       Union[str, Any]          =       na_probs[qid] > na_prob_thresh
						if pred_na:
									UpperCAmelCase_      :       List[str]          =       float(not qid_to_has_ans[qid]		)
						else:
									UpperCAmelCase_      :       Any          =       s
			return new_scores
def __UpperCAmelCase   (					A					:			Any			,       A					:			Any			,       A					:			str=None		)    ->   int:
			if not qid_list:
						UpperCAmelCase_      :       str          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores.values()		) / total),
						        ('''f1''', 100.0 * sum(fa_scores.values()		) / total),
						        ('''total''', total),
						    ]		)
			else:
						UpperCAmelCase_      :       Dict          =       len(A		)
						return collections.OrderedDict(
						    [
						        ('''exact''', 100.0 * sum(exact_scores[k] for k in qid_list		) / total),
						        ('''f1''', 100.0 * sum(fa_scores[k] for k in qid_list		) / total),
						        ('''total''', total),
						    ]		)
def __UpperCAmelCase   (					A					:			Union[str, Any]			,       A					:			Dict			,       A					:			Union[str, Any]		)    ->   Dict:
			for k in new_eval:
						UpperCAmelCase_      :       str          =       new_eval[k]
def __UpperCAmelCase   (					A					:			str			,       A					:			Tuple			,       A					:			Any			,       A					:			List[str]		)    ->   Optional[int]:
			plt.step(A			,       A			,       color='''b'''			,       alpha=0.2			,       where='''post'''		)
			plt.fill_between(A			,       A			,       step='''post'''			,       alpha=0.2			,       color='''b'''		)
			plt.xlabel('''Recall'''		)
			plt.ylabel('''Precision'''		)
			plt.xlim([0.0, 1.05]		)
			plt.ylim([0.0, 1.05]		)
			plt.title(A		)
			plt.savefig(A		)
			plt.clf()
def __UpperCAmelCase   (					A					:			Optional[Any]			,       A					:			Optional[Any]			,       A					:			Optional[int]			,       A					:			Optional[int]			,       A					:			str=None			,       A					:			Any=None		)    ->   Any:
			UpperCAmelCase_      :       Optional[Any]          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			UpperCAmelCase_      :       int          =       0.0
			UpperCAmelCase_      :       List[Any]          =       1.0
			UpperCAmelCase_      :       Optional[int]          =       0.0
			UpperCAmelCase_      :       Optional[Any]          =       [1.0]
			UpperCAmelCase_      :       List[Any]          =       [0.0]
			UpperCAmelCase_      :       Dict          =       0.0
			for i, qid in enumerate(A		):
						if qid_to_has_ans[qid]:
									true_pos += scores[qid]
						UpperCAmelCase_      :       List[Any]          =       true_pos / float(i + 1		)
						UpperCAmelCase_      :       Union[str, Any]          =       true_pos / float(A		)
						if i == len(A		) - 1 or na_probs[qid] != na_probs[qid_list[i + 1]]:
									# i.e., if we can put a threshold after this point
									avg_prec += cur_p * (cur_r - recalls[-1])
									precisions.append(A		)
									recalls.append(A		)
			if out_image:
						plot_pr_curve(A			,       A			,       A			,       A		)
			return {"ap": 100.0 * avg_prec}
def __UpperCAmelCase   (					A					:			List[Any]			,       A					:			str			,       A					:			Any			,       A					:			Optional[int]			,       A					:			Any			,       A					:			List[str]		)    ->   List[Any]:
			if out_image_dir and not os.path.exists(A		):
						os.makedirs(A		)
			UpperCAmelCase_      :       Any          =       sum(1 for v in qid_to_has_ans.values() if v		)
			if num_true_pos == 0:
						return
			UpperCAmelCase_      :       Union[str, Any]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_exact.png'''		)			,       title='''Precision-Recall curve for Exact Match score'''			,       )
			UpperCAmelCase_      :       List[str]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_f1.png'''		)			,       title='''Precision-Recall curve for F1 score'''			,       )
			UpperCAmelCase_      :       Dict          =       {k: float(A		) for k, v in qid_to_has_ans.items()}
			UpperCAmelCase_      :       Optional[int]          =       make_precision_recall_eval(
			    A			,       A			,       A			,       A			,       out_image=os.path.join(A			,       '''pr_oracle.png'''		)			,       title='''Oracle Precision-Recall curve (binary task of HasAns vs. NoAns)'''			,       )
			merge_eval(A			,       A			,       '''pr_exact'''		)
			merge_eval(A			,       A			,       '''pr_f1'''		)
			merge_eval(A			,       A			,       '''pr_oracle'''		)
def __UpperCAmelCase   (					A					:			List[str]			,       A					:			Tuple			,       A					:			str			,       A					:			List[Any]		)    ->   Dict:
			if not qid_list:
						return
			UpperCAmelCase_      :       List[Any]          =       [na_probs[k] for k in qid_list]
			UpperCAmelCase_      :       Union[str, Any]          =       np.ones_like(A		) / float(len(A		)		)
			plt.hist(A			,       weights=A			,       bins=2_0			,       range=(0.0, 1.0)		)
			plt.xlabel('''Model probability of no-answer'''		)
			plt.ylabel('''Proportion of dataset'''		)
			plt.title(F"Histogram of no-answer probability: {name}"		)
			plt.savefig(os.path.join(A			,       F"na_prob_hist_{name}.png"		)		)
			plt.clf()
def __UpperCAmelCase   (					A					:			str			,       A					:			Dict			,       A					:			List[str]			,       A					:			int		)    ->   Optional[Any]:
			UpperCAmelCase_      :       Tuple          =       sum(1 for k in qid_to_has_ans if not qid_to_has_ans[k]		)
			UpperCAmelCase_      :       Optional[Any]          =       num_no_ans
			UpperCAmelCase_      :       int          =       cur_score
			UpperCAmelCase_      :       List[Any]          =       0.0
			UpperCAmelCase_      :       Dict          =       sorted(A			,       key=lambda A		: na_probs[k]		)
			for i, qid in enumerate(A		):
						if qid not in scores:
									continue
						if qid_to_has_ans[qid]:
									UpperCAmelCase_      :       Tuple          =       scores[qid]
						else:
									if preds[qid]:
												UpperCAmelCase_      :       Optional[int]          =       -1
									else:
												UpperCAmelCase_      :       int          =       0
						cur_score += diff
						if cur_score > best_score:
									UpperCAmelCase_      :       Dict          =       cur_score
									UpperCAmelCase_      :       Dict          =       na_probs[qid]
			return 100.0 * best_score / len(A		), best_thresh
def __UpperCAmelCase   (					A					:			int			,       A					:			List[Any]			,       A					:			Union[str, Any]			,       A					:			Tuple			,       A					:			str			,       A					:			Union[str, Any]		)    ->   Tuple:
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[int]          =       find_best_thresh(A			,       A			,       A			,       A		)
			UpperCAmelCase_      :       List[str]          =       best_exact
			UpperCAmelCase_      :       List[Any]          =       exact_thresh
			UpperCAmelCase_      :       Any          =       best_fa
			UpperCAmelCase_      :       str          =       fa_thresh
def __UpperCAmelCase   (					)    ->   Tuple:
			with open(OPTS.data_file		) as f:
						UpperCAmelCase_      :       Optional[int]          =       json.load(A		)
						UpperCAmelCase_      :       str          =       dataset_json['''data''']
			with open(OPTS.pred_file		) as f:
						UpperCAmelCase_      :       List[Any]          =       json.load(A		)
			if OPTS.na_prob_file:
						with open(OPTS.na_prob_file		) as f:
									UpperCAmelCase_      :       List[str]          =       json.load(A		)
			else:
						UpperCAmelCase_      :       List[str]          =       {k: 0.0 for k in preds}
			UpperCAmelCase_      :       Optional[Any]          =       make_qid_to_has_ans(A		)  # maps qid to True/False
			UpperCAmelCase_      :       Optional[int]          =       [k for k, v in qid_to_has_ans.items() if v]
			UpperCAmelCase_      :       Dict          =       [k for k, v in qid_to_has_ans.items() if not v]
			UpperCAmelCase_    ,      UpperCAmelCase_      :       List[Any]          =       get_raw_scores(A			,       A		)
			UpperCAmelCase_      :       Optional[int]          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       Tuple          =       apply_no_ans_threshold(A			,       A			,       A			,       OPTS.na_prob_thresh		)
			UpperCAmelCase_      :       int          =       make_eval_dict(A			,       A		)
			if has_ans_qids:
						UpperCAmelCase_      :       Optional[int]          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''HasAns'''		)
			if no_ans_qids:
						UpperCAmelCase_      :       Dict          =       make_eval_dict(A			,       A			,       qid_list=A		)
						merge_eval(A			,       A			,       '''NoAns'''		)
			if OPTS.na_prob_file:
						find_all_best_thresh(A			,       A			,       A			,       A			,       A			,       A		)
			if OPTS.na_prob_file and OPTS.out_image_dir:
						run_precision_recall_analysis(A			,       A			,       A			,       A			,       A			,       OPTS.out_image_dir		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''hasAns'''		)
						histogram_na_prob(A			,       A			,       OPTS.out_image_dir			,       '''noAns'''		)
			if OPTS.out_file:
						with open(OPTS.out_file			,       '''w'''		) as f:
									json.dump(A			,       A		)
			else:
						print(json.dumps(A			,       indent=2		)		)


if __name__ == "__main__":
						_UpperCamelCase						:						Union[str, Any]  =					parse_args()
						if OPTS.out_image_dir:
												import matplotlib

												matplotlib.use('Agg')
												import matplotlib.pyplot as plt
						main()










'''simple docstring'''





import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
						from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor


class  snake_case__      :




					def __init__(  self  :		Union[str, Any]				, _A  :		Optional[Any]				, _A  :		Dict=2				, _A  :		Optional[Any]=32				, _A  :		List[str]=16				, _A  :		str=3				, _A  :		List[Any]=True				, _A  :		Optional[int]=True				, _A  :		Optional[int]=32				, _A  :		Optional[Any]=4				, _A  :		Optional[int]=[0, 1, 2, 3]				, _A  :		List[Any]=4				, _A  :		Optional[int]=37				, _A  :		Optional[Any]="gelu"				, _A  :		Optional[Any]=0.1				, _A  :		Union[str, Any]=0.1				, _A  :		Dict=0.02				, _A  :		Optional[Any]=3				, _A  :		Union[str, Any]=[1, 3_84, 24, 24]				, _A  :		int=True				, _A  :		int=None				, )    ->							Tuple:
								UpperCAmelCase_      :       Tuple          =       parent
								UpperCAmelCase_      :       List[str]          =       batch_size
								UpperCAmelCase_      :       Union[str, Any]          =       image_size
								UpperCAmelCase_      :       List[Any]          =       patch_size
								UpperCAmelCase_      :       Dict          =       num_channels
								UpperCAmelCase_      :       str          =       is_training
								UpperCAmelCase_      :       List[str]          =       use_labels
								UpperCAmelCase_      :       Optional[Any]          =       hidden_size
								UpperCAmelCase_      :       str          =       num_hidden_layers
								UpperCAmelCase_      :       int          =       backbone_out_indices
								UpperCAmelCase_      :       str          =       num_attention_heads
								UpperCAmelCase_      :       List[Any]          =       intermediate_size
								UpperCAmelCase_      :       Union[str, Any]          =       hidden_act
								UpperCAmelCase_      :       Dict          =       hidden_dropout_prob
								UpperCAmelCase_      :       Union[str, Any]          =       attention_probs_dropout_prob
								UpperCAmelCase_      :       List[str]          =       initializer_range
								UpperCAmelCase_      :       int          =       num_labels
								UpperCAmelCase_      :       Union[str, Any]          =       backbone_featmap_shape
								UpperCAmelCase_      :       List[str]          =       scope
								UpperCAmelCase_      :       List[str]          =       is_hybrid
								# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
								UpperCAmelCase_      :       List[Any]          =       (image_size // patch_size) ** 2
								UpperCAmelCase_      :       List[str]          =       num_patches + 1




					def 		A      (  self  :		Dict      )    ->							Optional[int]:
								UpperCAmelCase_      :       Optional[int]          =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

								UpperCAmelCase_      :       List[str]          =       None
								if self.use_labels:
											UpperCAmelCase_      :       Optional[int]          =       ids_tensor([self.batch_size, self.image_size, self.image_size]				, self.num_labels      )

								UpperCAmelCase_      :       Dict          =       self.get_config()

								return config, pixel_values, labels




					def 		A      (  self  :		int      )    ->							Optional[Any]:
								UpperCAmelCase_      :       Optional[Any]          =       {
								    '''global_padding''': '''same''',
								    '''layer_type''': '''bottleneck''',
								    '''depths''': [3, 4, 9],
								    '''out_features''': ['''stage1''', '''stage2''', '''stage3'''],
								    '''embedding_dynamic_padding''': True,
								    '''hidden_sizes''': [96, 1_92, 3_84, 7_68],
								    '''num_groups''': 2,
								}

								return DPTConfig(
								    image_size=self.image_size				, patch_size=self.patch_size				, num_channels=self.num_channels				, hidden_size=self.hidden_size				, num_hidden_layers=self.num_hidden_layers				, backbone_out_indices=self.backbone_out_indices				, num_attention_heads=self.num_attention_heads				, intermediate_size=self.intermediate_size				, hidden_act=self.hidden_act				, hidden_dropout_prob=self.hidden_dropout_prob				, attention_probs_dropout_prob=self.attention_probs_dropout_prob				, is_decoder=_A				, initializer_range=self.initializer_range				, is_hybrid=self.is_hybrid				, backbone_config=_A				, backbone_featmap_shape=self.backbone_featmap_shape				, )




					def 		A      (  self  :		Union[str, Any]				, _A  :		List[Any]				, _A  :		Optional[int]				, _A  :		Optional[int]      )    ->							Union[str, Any]:
								UpperCAmelCase_      :       Optional[int]          =       DPTModel(config=_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       List[str]          =       model(_A      )
								self.parent.assertEqual(result.last_hidden_state.shape				, (self.batch_size, self.seq_length, self.hidden_size)      )




					def 		A      (  self  :		Tuple				, _A  :		str				, _A  :		Tuple				, _A  :		Union[str, Any]      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.num_labels
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       int          =       model(_A      )
								self.parent.assertEqual(result.predicted_depth.shape				, (self.batch_size, self.image_size, self.image_size)      )




					def 		A      (  self  :		List[str]				, _A  :		str				, _A  :		Union[str, Any]				, _A  :		int      )    ->							Dict:
								UpperCAmelCase_      :       int          =       self.num_labels
								UpperCAmelCase_      :       Optional[Any]          =       DPTForSemanticSegmentation(_A      )
								model.to(_A      )
								model.eval()
								UpperCAmelCase_      :       Tuple          =       model(_A				, labels=_A      )
								self.parent.assertEqual(
								    result.logits.shape				, (self.batch_size, self.num_labels, self.image_size, self.image_size)      )





					def 		A      (  self  :		Dict      )    ->							Any:
								UpperCAmelCase_      :       Optional[int]          =       self.prepare_config_and_inputs()
								UpperCAmelCase_    ,      UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       config_and_inputs
								UpperCAmelCase_      :       Tuple          =       {'''pixel_values''': pixel_values}
								return config, inputs_dict






@require_torch
class  snake_case__      (					UpperCamelCase     ,				UpperCamelCase     ,				unittest.TestCase):
					a_        =					(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
					a_        =					(
					    {
					        "depth-estimation": DPTForDepthEstimation,
					        "feature-extraction": DPTModel,
					        "image-segmentation": DPTForSemanticSegmentation,
					    }
					    if is_torch_available()
					    else {}
					)

					a_        =					False
					a_        =					False
					a_        =					False




					def 		A      (  self  :		Tuple      )    ->							Tuple:
								UpperCAmelCase_      :       Any          =       DPTModelTester(self      )
								UpperCAmelCase_      :       Dict          =       ConfigTester(self				, config_class=_A				, has_text_modality=_A				, hidden_size=37      )




					def 		A      (  self  :		Optional[int]      )    ->							int:
								self.config_tester.run_common_tests()




					@unittest.skip(reason='''DPT does not use inputs_embeds'''      )
					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								pass




					def 		A      (  self  :		str      )    ->							Dict:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Any          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Union[str, Any]          =       model_class(_A      )
											self.assertIsInstance(model.get_input_embeddings()				, (nn.Module)      )
											UpperCAmelCase_      :       Dict          =       model.get_output_embeddings()
											self.assertTrue(x is None or isinstance(_A				, nn.Linear      )      )




					def 		A      (  self  :		List[Any]      )    ->							Tuple:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       Dict          =       model_class(_A      )
											UpperCAmelCase_      :       Optional[Any]          =       inspect.signature(model.forward      )
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											UpperCAmelCase_      :       int          =       [*signature.parameters.keys()]

											UpperCAmelCase_      :       Any          =       ['''pixel_values''']
											self.assertListEqual(arg_names[:1]				, _A      )




					def 		A      (  self  :		List[str]      )    ->							str:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_model(*_A      )




					def 		A      (  self  :		str      )    ->							List[str]:
								UpperCAmelCase_      :       List[Any]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_depth_estimation(*_A      )




					def 		A      (  self  :		str      )    ->							List[Any]:
								UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs()
								self.model_tester.create_and_check_for_semantic_segmentation(*_A      )




					def 		A      (  self  :		Tuple      )    ->							str:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       int          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       Union[str, Any]          =       True

											if model_class in get_values(_A      ):
														continue

											UpperCAmelCase_      :       Optional[int]          =       model_class(_A      )
											model.to(_A      )
											model.train()
											UpperCAmelCase_      :       Any          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       Tuple          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Union[str, Any]      )    ->							Tuple:
								for model_class in self.all_model_classes:
											if model_class.__name__ == "DPTForDepthEstimation":
														continue

											UpperCAmelCase_    ,      UpperCAmelCase_      :       Tuple          =       self.model_tester.prepare_config_and_inputs_for_common()
											UpperCAmelCase_      :       List[Any]          =       False
											UpperCAmelCase_      :       Optional[Any]          =       True

											if model_class in get_values(_A      ) or not model_class.supports_gradient_checkpointing:
														continue
											UpperCAmelCase_      :       int          =       model_class(_A      )
											model.to(_A      )
											model.gradient_checkpointing_enable()
											model.train()
											UpperCAmelCase_      :       int          =       self._prepare_for_class(_A				, _A				, return_labels=_A      )
											UpperCAmelCase_      :       List[str]          =       model(**_A      ).loss
											loss.backward()




					def 		A      (  self  :		Optional[Any]      )    ->							Any:
								UpperCAmelCase_    ,      UpperCAmelCase_      :       List[str]          =       self.model_tester.prepare_config_and_inputs_for_common()

								UpperCAmelCase_      :       List[str]          =       _config_zero_init(_A      )
								for model_class in self.all_model_classes:
											UpperCAmelCase_      :       int          =       model_class(config=_A      )
											# Skip the check for the backbone
											UpperCAmelCase_      :       List[str]          =       []
											for name, module in model.named_modules():
														if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																	UpperCAmelCase_      :       Union[str, Any]          =       [F"{name}.{key}" for key in module.state_dict().keys()]
																	break

											for name, param in model.named_parameters():
														if param.requires_grad:
																	if name in backbone_params:
																				continue
																	self.assertIn(
																	    ((param.data.mean() * 1e9).round() / 1e9).item()				, [0.0, 1.0]				, msg=F"Parameter {name} of model {model_class} seems not properly initialized"				, )




					@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''      )
					def 		A      (  self  :		Tuple      )    ->							Optional[Any]:
								pass




					@slow
					def 		A      (  self  :		Any      )    ->							int:
								for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[1:]:
											UpperCAmelCase_      :       Any          =       DPTModel.from_pretrained(_A      )
											self.assertIsNotNone(_A      )





					def 		A      (  self  :		Union[str, Any]      )    ->							str:
								# We do this test only for DPTForDepthEstimation since it is the only model that uses readout_type
								UpperCAmelCase_    ,      UpperCAmelCase_      :       Optional[Any]          =       self.model_tester.prepare_config_and_inputs_for_common()
								UpperCAmelCase_      :       Optional[int]          =       '''add'''
								with self.assertRaises(_A      ):
											UpperCAmelCase_      :       Optional[Any]          =       DPTForDepthEstimation(_A      )






def __UpperCAmelCase   (					)    ->   List[Any]:
			UpperCAmelCase_      :       List[Any]          =       Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''		)
			return image







@require_torch
@require_vision
@slow
class  snake_case__      (					unittest.TestCase):




					def 		A      (  self  :		Union[str, Any]      )    ->							List[Any]:
								UpperCAmelCase_      :       List[Any]          =       DPTImageProcessor.from_pretrained('''Intel/dpt-hybrid-midas'''      )
								UpperCAmelCase_      :       Any          =       DPTForDepthEstimation.from_pretrained('''Intel/dpt-hybrid-midas'''      ).to(_A      )

								UpperCAmelCase_      :       Tuple          =       prepare_img()
								UpperCAmelCase_      :       Union[str, Any]          =       image_processor(images=_A				, return_tensors='''pt'''      ).to(_A      )

								# forward pass
								with torch.no_grad():
											UpperCAmelCase_      :       str          =       model(**_A      )
											UpperCAmelCase_      :       List[Any]          =       outputs.predicted_depth

								# verify the predicted depth
								UpperCAmelCase_      :       str          =       torch.Size((1, 3_84, 3_84)      )
								self.assertEqual(predicted_depth.shape				, _A      )

								UpperCAmelCase_      :       Tuple          =       torch.tensor(
								    [[[5.6_437, 5.6_146, 5.6_511], [5.4_371, 5.5_649, 5.5_958], [5.5_215, 5.5_184, 5.5_293]]]      ).to(_A      )

								self.assertTrue(torch.allclose(outputs.predicted_depth[:3, :3, :3] / 1_00				, _A				, atol=1e-4      )      )


